Information diffusion in online social networks is affected by the underlying network
topology, but it also has the power to change it. Online users are constantly creating new …

We propose the first multistage intervention framework that tackles fake news in social
networks by combining reinforcement learning with a point process network activity model …

In a wide variety of applications, humans interact with a complex environment by means of
asynchronous stochastic discrete events in continuous time. Can we design online …

We consider control problems for multi-stage campaigning over social networks. The
dynamic programming framework is employed to balance the high present reward and large …

Users in social networks whose posts stay at the top of their followers' feeds the longest time
are more likely to be noticed. Can we design an online algorithm to help them decide when …

Online knowledge repositories typically rely on their users or dedicated editors to evaluate
the reliability of their contents. These explicit feedback mechanisms can be viewed as noisy …

Modeling the popularity dynamics of an online item is an important open problem in
computational social science. This paper presents an in-depth study of popularity dynamics …

User engagement in online social networking depends critically on the level of social activity
in the corresponding platform--the number of online actions, such as posts, shares or …

We propose a general framework of using multi-level log-Gaussian Cox process to model
repeatedly observed point processes with complex structures; such type of data have …

Temporal point processes have been widely applied to model event sequence data
generated by online users. In this paper, we consider the problem of how to design the …